code: def test_check_params(art_warning, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(
              OverTheAirFlickeringPyTorch
          )
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, eps_step="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, eps_step=-0.5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, max_iter="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, max_iter=-5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_0="test")
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_0=-0.5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_1="test")
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_1=-0.5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_2="test")
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, beta_2=-0.5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, loss_margin="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, loss_margin=-0.5
              )
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, batch_size="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, batch_size=-5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, start_frame_index="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, start_frame_index=-0.5
              )
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, num_frames=5.0
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(classifier, num_frames=-5)
  
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, round_samples="test"
              )
          with pytest.raises(ValueError):
              _ = OverTheAirFlickeringPyTorch(
                  classifier, round_samples=-5
              )
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_over_the_air.py
imports: import logging
  import numpy
  import pytest
  import torch
  from art.attacks.evasion.over_the_air_flickering.over_the_air_flickering_pytorch import OverTheAirFlickeringPyTorch
  from art.estimators.classification import PyTorchClassifier
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException
label_desc: Use image node_id 3 for calling a global function with example usage: test_check_params(art_warning, image_dl_estimator_for_attack) without return types